ANALYSIS REPORT
Ambroise
29 mars, 2022
Setup
library(repro)
# # load packages from yaml header
# automate_load_packages()
# # include external scripts
# automate_load_scripts()
# load data
data_ambroise_full <- haven::read_sav("Enquete_Ambroise.sav")
pacman::p_load(papaja, tinylabels, apaTables, haven, ggplot2, tidyverse, gtsummary, car, GGally, MASS, matrixStats, rcompanion, moments, utils, sjPlot, interactions, kableExtra)
sessio = sessionInfo(); #opts_chunk$set(echo = F, message=F, warning=F) # set echo F for allThis file was automatically created via the Repro package (version 0.1.0) using R version 4.0.1 (2020-06-06)
options(scipen = 666, warn=-1, contrasts=c("contr.sum","contr.poly"), knitr.kable.NA = '') #remove scientific notation # remove warnings #set contrasts to sum !
set.seed(666) #set random seed
# panderOptions('knitr.auto.asis', FALSE) #remove auto styling
# Look at R/clean.R (listed in the YAML) which does all the preprocessing for more info
# If you are unsure weather or not you have `git` `make` & `docker`.
# check_git()
# check_make()
# check_docker()Introduction
Blabla
Demographics
base[c("Age", "Gender", "Profession")] %>% tbl_summary(statistic = list(all_continuous() ~ "{mean} ({sd})", all_categorical() ~ "{n} / {N} ({p}%)"),) %>% modify_caption("**Table 1. Demographics **") %>%
bold_labels()| Characteristic | N = 3031 |
|---|---|
| Age | 21.7 (4.4) |
| Gender | |
| Homme | 56 / 303 (18%) |
| Femme | 244 / 303 (81%) |
| Autre/NA | 3 / 303 (1.0%) |
| Profession | |
| Etudiant.e | 298 / 303 (98%) |
| Actif.ve | 3 / 303 (1.0%) |
| Les deux | 2 / 303 (0.7%) |
|
1
Mean (SD); n / N (%)
|
|
base[c("Decision Mode")] %>% tbl_summary(statistic = list(all_continuous() ~ "{mean} ({sd})", all_categorical() ~ "{n} / {N} ({p}%)"),) %>% modify_caption("**Table 2. Decision Mode **") %>%
bold_labels()| Characteristic | N = 3031 |
|---|---|
| Decision Mode | |
| Affectif | 32 / 303 (11%) |
| Les deux | 208 / 303 (69%) |
| Cognitif | 63 / 303 (21%) |
|
1
n / N (%)
|
|
\(~\) \(~\)
base_clean[c("Age", "Gender", "Profession")] %>% tbl_summary(statistic = list(all_continuous() ~ "{mean} ({sd})", all_categorical() ~ "{n} / {N} ({p}%)"),) %>% modify_caption("**Table 3. Demographics after cleaning**") %>%
bold_labels()| Characteristic | N = 2961 |
|---|---|
| Age | 21.8 (4.3) |
| Gender | |
| Homme | 55 / 296 (19%) |
| Femme | 239 / 296 (81%) |
| Autre/NA | 2 / 296 (0.7%) |
| Profession | |
| Etudiant.e | 291 / 296 (98%) |
| Actif.ve | 3 / 296 (1.0%) |
| Les deux | 2 / 296 (0.7%) |
|
1
Mean (SD); n / N (%)
|
|
base_clean[c("Decision Mode")] %>% tbl_summary(statistic = list(all_continuous() ~ "{mean} ({sd})", all_categorical() ~ "{n} / {N} ({p}%)"),) %>% modify_caption("**Table 4. Decision Mode after cleaning**") %>%
bold_labels()| Characteristic | N = 2961 |
|---|---|
| Decision Mode | |
| Affectif | 32 / 296 (11%) |
| Les deux | 202 / 296 (68%) |
| Cognitif | 62 / 296 (21%) |
|
1
n / N (%)
|
|
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Statistics
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Price
base_price = base_clean #no huge outliers, see appendix
final_price = lm(Price ~ Priming*Product*score + Age + Decision_mode, data = base_price)
apa_lm <- apa_print(anova(final_price))
apa_table(apa_lm$table, caption = "Anova table for Price.")
Anova table for Price.
| Effect | \(\hat{\eta}^2_G\) | 90% CI | \(F\) | \(\mathit{df}\) | \(\mathit{df}_{\mathrm{res}}\) | \(p\) |
|---|---|---|---|---|---|---|
| Priming | .001 | [.000, .017] | 0.35 | 1 | 285 | .553 |
| Product | .009 | [.000, .037] | 2.70 | 1 | 285 | .102 |
| Score | .001 | [.000, .017] | 0.37 | 1 | 285 | .546 |
| Age | .007 | [.000, .032] | 1.99 | 1 | 285 | .160 |
| Decision mode | .003 | [.000, .017] | 0.44 | 2 | 285 | .643 |
| Priming \(\times\) Product | .006 | [.000, .030] | 1.79 | 1 | 285 | .182 |
| Priming \(\times\) Score | .014 | [.000, .044] | 3.92 | 1 | 285 | .049 |
| Product \(\times\) Score | .001 | [.000, .015] | 0.24 | 1 | 285 | .624 |
| Priming \(\times\) Product \(\times\) Score | .009 | [.000, .035] | 2.49 | 1 | 285 | .116 |
plot_model(final_price)
interactions::cat_plot(final_price, pred = Priming, modx = Product, interval = TRUE, plot.points = T, point.alpha =0.3, pred.labels = c("Computational","Affective"), modx.labels = c("A+","A++"), errorbar.width = 0.4, y.label = "Price Evaluation") 
interactions::interact_plot(final_price, pred = score, modx = Priming, interval = TRUE, plot.points = T, modx.labels = c("Computational","Affective"), point.alpha =0.3, x.label = "Affective Score towards climate change", y.label = "Price Evaluation") 
interactions::interact_plot(final_price, pred = score, modx = Priming, interval = TRUE, plot.points = F, modx.labels = c("Computational","Affective"), point.alpha =0.3, x.label = "Affective Score towards climate change", y.label = "Price Evaluation") 
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Example for reporting :
The priming condition (\(F(1, 285) = 0.35\), \(p = .553\), \(\hat{\eta}^2_G = .001\), 90% CI \([.000, .017]\)) and the affective score towards climate change () did not affect price evaluation by themselves. However, the effect of affective score towards climate change on the price evalution slightly differed by priming condition, \(F(1, 285) = 3.92\), \(p = .049\), \(\hat{\eta}^2_G = .014\), 90% CI \([.000, .044]\).
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However it is important to note the very small effect size \(\hat{\eta}^2_G = .014\) and that the confidence intervals of the effect size includes 0 90% CI \([.000, .044]\) . \(~\)
Grand biais vers 0 avec les positive affect scale. Resultat pas très interpretable donc… Mais je laisse les deux graphs au cas ou.
\(~\) \(~\)
Probability to buy
base_buy = filter(base_clean, id %notin% c("13804")) #remove huge outliers, see appendix
final_buy = lm(Buy ~ Priming*Product*score + Age + Decision_mode, data = base_buy)
apa_lm <- apa_print(anova(final_buy))
apa_table(apa_lm$table, caption = "Anova table for Probability to buy.")
Anova table for Probability to buy.
| Effect | \(\hat{\eta}^2_G\) | 90% CI | \(F\) | \(\mathit{df}\) | \(\mathit{df}_{\mathrm{res}}\) | \(p\) |
|---|---|---|---|---|---|---|
| Priming | .000 | [.000, .010] | 0.06 | 1 | 284 | .809 |
| Product | .037 | [.010, .080] | 11.01 | 1 | 284 | .001 |
| Score | .001 | [.000, .013] | 0.15 | 1 | 284 | .701 |
| Age | .034 | [.008, .075] | 9.88 | 1 | 284 | .002 |
| Decision mode | .000 | [.000, .000] | 0.04 | 2 | 284 | .965 |
| Priming \(\times\) Product | .000 | [.000, .011] | 0.08 | 1 | 284 | .773 |
| Priming \(\times\) Score | .010 | [.000, .037] | 2.74 | 1 | 284 | .099 |
| Product \(\times\) Score | .000 | [.000, .012] | 0.12 | 1 | 284 | .728 |
| Priming \(\times\) Product \(\times\) Score | .007 | [.000, .033] | 2.12 | 1 | 284 | .147 |
sjPlot::plot_model(final_buy)
interactions::cat_plot(final_buy, pred = Product, interval = TRUE, plot.points = T, point.alpha =0.3, pred.labels = c("A+","A++"), errorbar.width = 0.4, y.label = "Probability to buy") 
sjPlot::plot_model(final_buy, type = "pred", show.data = T)[4]
interactions::cat_plot(final_buy, pred = Priming, modx = Product, interval = TRUE, plot.points = T, point.alpha =0.3, pred.labels = c("Computational","Affective"), modx.labels = c("A+","A++"), errorbar.width = 0.4, y.label = "Probability to buy") 
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Reccomendation
base_recco = base_clean #no huge outliers, see appendix
final_recco = lm(Reccomend ~ Priming*Product*score + Decision_mode + Age, data = base_recco)
apa_lm <- apa_print(anova(final_recco))
apa_table(apa_lm$table, caption = "Anova table for Reccomendation.")
Anova table for Reccomendation.
| Effect | \(\hat{\eta}^2_G\) | 90% CI | \(F\) | \(\mathit{df}\) | \(\mathit{df}_{\mathrm{res}}\) | \(p\) |
|---|---|---|---|---|---|---|
| Priming | .002 | [.000, .019] | 0.54 | 1 | 285 | .461 |
| Product | .087 | [.042, .143] | 27.31 | 1 | 285 | < .001 |
| Score | .002 | [.000, .019] | 0.54 | 1 | 285 | .463 |
| Decision mode | .001 | [.000, .002] | 0.12 | 2 | 285 | .885 |
| Age | .008 | [.000, .035] | 2.40 | 1 | 285 | .122 |
| Priming \(\times\) Product | .000 | [.000, .000] | 0.00 | 1 | 285 | .982 |
| Priming \(\times\) Score | .001 | [.000, .017] | 0.38 | 1 | 285 | .537 |
| Product \(\times\) Score | .004 | [.000, .026] | 1.25 | 1 | 285 | .265 |
| Priming \(\times\) Product \(\times\) Score | .007 | [.000, .032] | 2.04 | 1 | 285 | .154 |
plot_model(final_recco)
interactions::cat_plot(final_recco, pred = Product, interval = TRUE, plot.points = T, point.alpha =0.3, pred.labels = c("A+","A++"), errorbar.width = 0.4, y.label = "Reccomendation to buy") 
# sjPlot::plot_model(final_recco, type = "pred", show.data = T)[5]
interactions::cat_plot(final_recco, pred = Priming, modx = Product, interval = TRUE, plot.points = T, point.alpha =0.3, pred.labels = c("Computational","Affective"), modx.labels = c("A+","A++"), errorbar.width = 0.4, y.label = "Reccomendation to buy") 
# interactions::interact_plot(final_recco, pred = negatif_affect, modx = Product, interval = TRUE, plot.points = T, point.alpha =0.3, modx.labels = c("A+","A++"), x.label = "Negative affect towards climate change", y.label = "Reccomendation to buy")
#
# interactions::interact_plot(final_recco, pred = negatif_affect, modx = Product, interval = TRUE, plot.points = F, point.alpha =0.3, modx.labels = c("A+","A++"), x.label = "Negative affect towards climate change", y.label = "Reccomendation to buy") \(~\) \(~\)
Ecologic Evaluation
base_eco = filter(base_clean, id %notin% c("13594", "11200", "13804", "12529", "11017")) #remove huge outliers, see appendix
final_eco = lm(Ecolo_Eval ~ Priming*Product*score + Age + Decision_mode, data = base_eco)
apa_lm <- apa_print(anova(final_eco))
apa_table(apa_lm$table, caption = "Anova table for Ecological evaluation")
Anova table for Ecological evaluation
| Effect | \(\hat{\eta}^2_G\) | 90% CI | \(F\) | \(\mathit{df}\) | \(\mathit{df}_{\mathrm{res}}\) | \(p\) |
|---|---|---|---|---|---|---|
| Priming | .000 | [.000, .000] | 0.00 | 1 | 280 | .961 |
| Product | .215 | [.149, .283] | 76.68 | 1 | 280 | < .001 |
| Score | .001 | [.000, .016] | 0.26 | 1 | 280 | .614 |
| Age | .000 | [.000, .006] | 0.02 | 1 | 280 | .886 |
| Decision mode | .015 | [.000, .042] | 2.11 | 2 | 280 | .123 |
| Priming \(\times\) Product | .003 | [.000, .022] | 0.75 | 1 | 280 | .386 |
| Priming \(\times\) Score | .010 | [.000, .038] | 2.82 | 1 | 280 | .094 |
| Product \(\times\) Score | .000 | [.000, .000] | 0.00 | 1 | 280 | > .999 |
| Priming \(\times\) Product \(\times\) Score | .001 | [.000, .016] | 0.29 | 1 | 280 | .594 |
plot_model(final_eco)
interactions::cat_plot(final_eco, pred = Product, interval = TRUE, plot.points = T, point.alpha =0.3, pred.labels = c("A+","A++"), errorbar.width = 0.4, y.label = "Ecological Evaluation") 
interactions::cat_plot(final_eco, pred = Priming, modx = Product, interval = TRUE, plot.points = T, point.alpha =0.3, pred.labels = c("Computational","Affective"), modx.labels = c("A+","A++"), errorbar.width = 0.4, y.label = "Ecological Evaluation") 
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Appendix
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Score of Affect towards climate change
score = -negatif_affect score = score+positive_affect
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Plot All
base_clean %>%
keep(is.numeric) %>%
ggpairs()
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Check variables densities -> Positive and negative are not really good: to skewed. Score is quite better.¨
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# base_priceA = dplyr::filter(base_price, Product == "A++")
#
# final_priceA = lm(Price ~ Priming*Decision_mode + Age, data = base_priceA)
#
# apa_lm <- apa_print(anova(final_priceA))
# apa_table(apa_lm$table, caption = "Anova table for Price with only A++ product.")# plot_model(final_priceA)
#
# interactions::cat_plot(final_priceA, pred = Priming, interval = TRUE, plot.points = T, point.alpha =0.3, pred.labels = c("Computational","Affective"), errorbar.width = 0.4, y.label = "Price Evaluation") \(~\) \(~\)
# base_buyA = filter(base_buy, Product == "A++")
#
# final_buyA = lm(Buy ~ Priming*Decision_mode + Age , data = base_buyA)
#
# apa_lm <- apa_print(anova(final_buyA))
# apa_table(apa_lm$table, caption = "Anova table for Probability to buy only A++ product.")# plot_model(final_buyA)
#
# interactions::cat_plot(final_buyA, pred = Priming, interval = TRUE, plot.points = T, point.alpha =0.3, pred.labels = c("Computational","Affective"), errorbar.width = 0.4, y.label = "Probability to buy") \(~\) \(~\)
# base_reccoA = filter(base_recco, Product == "A++")
#
# final_reccoA = lm(Reccomend ~ Priming*Decision_mode + Age, data = base_reccoA)
#
# apa_lm <- apa_print(anova(final_reccoA))
# apa_table(apa_lm$table, caption = "Anova table for Reccomendation to buy only A++ product.")# plot_model(final_reccoA)
#
# interactions::cat_plot(final_reccoA, pred = Priming, interval = TRUE, plot.points = T, point.alpha =0.3, pred.labels = c("Computational","Affective"), errorbar.width = 0.4, y.label = "Reccomendation to buy") \(~\) \(~\)
# base_ecoA = filter(base_eco, Product == "A++")
#
#
# final_ecoA = lm(Ecolo_Eval ~ Priming*Decision_mode + Age, data = base_ecoA)
#
# apa_lm <- apa_print(anova(final_ecoA))
# apa_table(apa_lm$table, caption = "Anova table for Ecological evaluation only A++ product")# plot_model(final_ecoA)
#
# interactions::cat_plot(final_ecoA, pred = Priming, interval = TRUE, plot.points = T, point.alpha =0.3, pred.labels = c("Computational","Affective"), errorbar.width = 0.4, y.label = "Price Evaluation") \(~\) \(~\)
Correlation between affects towars climate change
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Test frequencies between priming groups (Positive affect)
| Characteristic | 1, N = 1701 | 2, N = 591 | 3, N = 251 | 4, N = 241 | 5, N = 161 | 6, N = 11 | 7, N = 11 | p-value2 |
|---|---|---|---|---|---|---|---|---|
| Priming | 0.2 | |||||||
| 0 | 89 (52%) | 33 (56%) | 13 (52%) | 9 (38%) | 4 (25%) | 1 (100%) | 1 (100%) | |
| 1 | 81 (48%) | 26 (44%) | 12 (48%) | 15 (62%) | 12 (75%) | 0 (0%) | 0 (0%) | |
|
1
n (%)
2
Fisher's exact test
|
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Test frequencies between priming groups (Negative affect)
| Characteristic | 1, N = 101 | 2, N = 91 | 3, N = 111 | 4, N = 321 | 5, N = 611 | 6, N = 991 | 7, N = 741 | p-value2 |
|---|---|---|---|---|---|---|---|---|
| Priming | 0.2 | |||||||
| 0 | 1 (10%) | 6 (67%) | 5 (45%) | 18 (56%) | 34 (56%) | 47 (47%) | 39 (53%) | |
| 1 | 9 (90%) | 3 (33%) | 6 (55%) | 14 (44%) | 27 (44%) | 52 (53%) | 35 (47%) | |
|
1
n (%)
2
Fisher's exact test
|
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Logistic regression to predict priming groups (Positive affect)
**
| Predictor | \(b\) | 95% CI | \(z\) | \(p\) | |
|---|---|---|---|---|---|
| 2 | Positive affect | 0.14 | [-0.04, 0.33] | 1.53 | .125 |
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Logistic regression to predict priming groups (Negative affect)
**
| Predictor | \(b\) | 95% CI | \(z\) | \(p\) | |
|---|---|---|---|---|---|
| 2 | Negatif affect | -0.07 | [-0.22, 0.09] | -0.87 | .386 |
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Logistic regression to predict priming groups (Affective Score)
**
| Predictor | \(b\) | 95% CI | \(z\) | \(p\) | |
|---|---|---|---|---|---|
| 2 | Score | 0.08 | [-0.03, 0.18] | 1.46 | .144 |
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\(~\) \(~\)
Visual check normality
plotNormalHistogram(base_clean$Price, main = "Price", sub = paste("skewness =", skewness(base_clean$Price, na.rm = TRUE)))
# print("Check price density by product")
# densityPlot(base_clean$Price, g = base_clean$Product) # by product
plotNormalHistogram(base_clean$Reccomend, main = "Reccomendation", sub = paste("skewness =", round(skewness(base_clean$Reccomend, na.rm = TRUE)),2))
plotNormalHistogram(base_clean$Buy, main = "Probability to buy", sub =
paste("skewness =", round(skewness(base_clean$Buy, na.rm = TRUE),2)))
plotNormalHistogram(base_clean$Ecolo_Eval, main = "Ecologic Evaluation", sub =
paste("skewness =", round(skewness(base_clean$Ecolo_Eval, na.rm = TRUE),2)))
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Model selection for price
| Stepwise Model Path Analysis of Deviance Table |
| Initial Model: |
| Price ~ Priming * Product * negatif_affect + Priming * Product * |
| positive_affect + Priming * Product * Decision_mode + Priming * |
| Product * Age |
| Final Model: |
| Price ~ Priming + Product + negatif_affect + positive_affect + |
| Priming:Product + Priming:negatif_affect + Product:negatif_affect + |
| Priming:positive_affect + Product:positive_affect + Priming:Product:negatif_affect |
| Step | Df | Deviance | Resid. Df | Resid. Dev | AIC |
|---|---|---|---|---|---|
| 272 | 1492.897 | 526.9619 | |||
|
2 | 1.2988527 | 274 | 1494.196 | 523.2193 |
|
2 | 1.1771062 | 276 | 1495.373 | 519.4524 |
|
2 | 2.1924218 | 278 | 1497.566 | 515.8860 |
|
2 | 5.3835937 | 280 | 1502.949 | 512.9482 |
|
1 | 0.0454730 | 281 | 1502.995 | 510.9572 |
|
1 | 0.1449535 | 282 | 1503.140 | 508.9857 |
|
1 | 1.2190939 | 283 | 1504.359 | 507.2257 |
|
1 | 1.8189887 | 284 | 1506.178 | 505.5834 |
|
1 | 3.3639817 | 285 | 1509.542 | 504.2438 |
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Price: Diagnostics Plots for linear model
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Model selection for Probality to buy
| Stepwise Model Path Analysis of Deviance Table |
| Initial Model: |
| Buy ~ Priming * Product * negatif_affect + Priming * Product * |
| positive_affect + Priming * Product * Decision_mode + Priming * |
| Product * Age |
| Final Model: |
| Buy ~ Priming + Product + negatif_affect + Age + Priming:negatif_affect + |
| Product:negatif_affect |
| Step | Df | Deviance | Resid. Df | Resid. Dev | AIC |
|---|---|---|---|---|---|
| 272 | 599.1365 | 256.7185 | |||
|
2 | 0.5872826 | 274 | 599.7238 | 253.0085 |
|
2 | 0.8112811 | 276 | 600.5351 | 249.4086 |
|
1 | 0.4022296 | 277 | 600.9373 | 247.6068 |
|
1 | 1.5140058 | 278 | 602.4513 | 246.3516 |
|
1 | 0.0234825 | 279 | 602.4748 | 244.3632 |
|
1 | 0.9743727 | 280 | 603.4492 | 242.8415 |
|
1 | 1.9725500 | 281 | 605.4217 | 241.8075 |
|
1 | 2.1206115 | 282 | 607.5423 | 240.8425 |
|
1 | 1.6186208 | 283 | 609.1610 | 239.6300 |
|
1 | 2.2333884 | 284 | 611.3944 | 238.7133 |
|
1 | 0.8585097 | 285 | 612.2529 | 237.1286 |
|
2 | 6.9999567 | 287 | 619.2528 | 236.4936 |
|
2 | 1.1043737 | 289 | 620.3572 | 233.0210 |
\(~\) \(~\)
Probability to buy: Diagnostics Plots for linear model
\(~\) \(~\)
Model selection for Reccomendation
| Stepwise Model Path Analysis of Deviance Table |
| Initial Model: |
| Reccomend ~ Priming * Product * negatif_affect + Priming * Product * |
| positive_affect + Priming * Product * Decision_mode + Priming * |
| Product * Age |
| Final Model: |
| Reccomend ~ Priming + Product + negatif_affect + positive_affect + |
| Age + Priming:Product + Product:negatif_affect + Priming:positive_affect + |
| Product:positive_affect + Priming:Age + Product:Age + Priming:Product:positive_affect + |
| Priming:Product:Age |
| Step | Df | Deviance | Resid. Df | Resid. Dev | AIC |
|---|---|---|---|---|---|
| 272 | 734.5904 | 317.0503 | |||
|
1 | 0.9862800 | 273 | 735.5767 | 315.4474 |
|
1 | 0.0347883 | 274 | 735.6115 | 313.4614 |
|
2 | 9.6592597 | 276 | 745.2707 | 313.3229 |
|
2 | 1.4877211 | 278 | 746.7585 | 309.9132 |
|
2 | 2.5010516 | 280 | 749.2595 | 306.9029 |
|
2 | 0.3121104 | 282 | 749.5716 | 303.0262 |
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Reccomendation: Diagnostics Plots for linear model AFTER
\(~\) \(~\)
Model selection for Ecologic Evaluation
| Stepwise Model Path Analysis of Deviance Table |
| Initial Model: |
| Ecolo_Eval ~ Priming * Product * negatif_affect + Priming * Product * |
| positive_affect + Priming * Product * Decision_mode + Priming * |
| Product * Age |
| Final Model: |
| Ecolo_Eval ~ Priming + Product + negatif_affect + positive_affect + |
| Decision_mode + Age + Priming:Product + Product:negatif_affect + |
| Priming:positive_affect + Product:positive_affect + Priming:Decision_mode + |
| Product:Decision_mode + Priming:Age + Product:Age + Priming:Product:Decision_mode + |
| Priming:Product:Age |
| Step | Df | Deviance | Resid. Df | Resid. Dev | AIC |
|---|---|---|---|---|---|
| 272 | 358.0852 | 104.36179 | |||
|
1 | 0.1295729 | 273 | 358.2148 | 102.46888 |
|
1 | 0.3551773 | 274 | 358.5699 | 100.76222 |
|
1 | 0.0511166 | 275 | 358.6210 | 98.80441 |
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Ecologic Evaluation: Diagnostics Plots for linear model AFTER
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Hollistic (Positive) Affect Towards Climate Change
plotNormalHistogram(base_clean$positive_affect, main = "Positive Affect", sub = paste("skewness =", skewness(base_clean$positive_affect, na.rm = TRUE))) 
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We see a strong right skew of the dristribution.
\(~\) \(~\)
Hollistic (Negative) Affect Towards Climate Change
plotNormalHistogram(base_clean$negatif_affect, main = "Negative Affect", sub = paste("skewness =", skewness(base_clean$negatif_affect, na.rm = TRUE))) 
\(~\) \(~\)
We see a slight left skew of the dristribution.
report::report_packages()## - repro (version 0.1.0; Aaron Peikert, Andreas Brandmaier and Caspar van Lissa, 2021)
## - papaja (version 0.1.0.9997; Aust et al., 2020)
## - GGally (version 2.1.2; Barret Schloerke et al., 2021)
## - tinylabels (version 0.2.3; Barth, 2022)
## - apaTables (version 2.0.8; David Stanley, 2021)
## - ggplot2 (version 3.3.5; Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.)
## - stringr (version 1.4.0; Hadley Wickham, 2019)
## - forcats (version 0.5.1; Hadley Wickham, 2021)
## - haven (version 2.4.3; Hadley Wickham and Evan Miller, 2021)
## - tidyr (version 1.2.0; Hadley Wickham and Maximilian Girlich, 2022)
## - readr (version 2.1.1; Hadley Wickham, Jim Hester and Jennifer Bryan, 2021)
## - dplyr (version 1.0.7; Hadley Wickham et al., 2021)
## - kableExtra (version 1.3.4; Hao Zhu, 2021)
## - matrixStats (version 0.61.0; Henrik Bengtsson, 2021)
## - car (version 3.0.12; John Fox and Sanford Weisberg, 2019)
## - carData (version 3.0.5; John Fox, Sanford Weisberg and Brad Price, 2022)
## - tibble (version 3.1.6; Kirill Müller and Hadley Wickham, 2021)
## - purrr (version 0.3.4; Lionel Henry and Hadley Wickham, 2020)
## - interactions (version 1.1.5; Long JA, 2019)
## - sjPlot (version 2.8.10; Lüdecke D, 2021)
## - moments (version 0.14; Lukasz Komsta and Frederick Novomestky, 2015)
## - R (version 4.0.1; R Core Team, 2020)
## - rcompanion (version 2.4.1; Salvatore Mangiafico, 2021)
## - gtsummary (version 1.5.0; Sjoberg DD, Whiting K, Curry M, Lavery JA, Larmarange Reproducible summary tables with the gtsummary package. The R Journal 2021;13:570–80. https://doi.org/10.32614/RJ-2021-053.)
## - MASS (version 7.3.51.6; Venables et al., 2002)
## - tidyverse (version 1.3.1; Wickham et al., 2019)